{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试与数据库相关的库函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.  从数据库获得tick数据并显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KQa/VoFVrwyuK1tStweP3RNxSW6cnvDIohjTa2izkBy2ctw+9zejE0QBsbtiB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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9e8050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从数据库获得价差数据\n",
    "from vtFunction import *\n",
    "from ctaBase import *\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "fields = ['datetime','askPrice1','bidPrice1','turnover','volume']\n",
    "symbol = u'rb1705'\n",
    "# 从数据库获得数据\n",
    "tickers1 = loadHistoryData(TICK_DB_NAME,symbol,'20161011 20:59:00','20161011 21:02:00',fields)\n",
    "buy = tickers1['bidPrice1']-tickers1['bidPrice1'].mean()\n",
    "sell = tickers1['askPrice1']-tickers1['bidPrice1'].mean()\n",
    "# 计算tick成交均价\n",
    "turnover = tickers1['turnover']-tickers1['turnover'].shift(1)\n",
    "turnover = turnover.apply(lambda x:max(x,0))\n",
    "volume = tickers1['volume']-tickers1['volume'].shift(1)\n",
    "volume = volume.apply(lambda x:max(x,1))\n",
    "aprice = turnover/volume/10\n",
    "aprice = aprice - tickers1['bidPrice1'].mean()\n",
    "aprice = aprice[aprice > -2]\n",
    "spread0 = aprice - buy - 10\n",
    "spread1 = aprice - sell - 10\n",
    "datas = pd.DataFrame({'buy':buy,'sell':sell,'aprice':aprice,'spread0':spread0,'spread1':spread1})\n",
    "# 画图\n",
    "import matplotlib.pyplot as plt\n",
    "plt.close()\n",
    "datas=datas.dropna(axis=0, how='any')\n",
    "datas\n",
    "datas.plot(kind='line')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 从数据库获得Day数据并显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04',\n",
      "               '2015-01-05'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-11-21</th>\n",
       "      <td>2783</td>\n",
       "      <td>2714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-22</th>\n",
       "      <td>2726</td>\n",
       "      <td>2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-23</th>\n",
       "      <td>2897</td>\n",
       "      <td>2954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-24</th>\n",
       "      <td>2950</td>\n",
       "      <td>2951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-25</th>\n",
       "      <td>2902</td>\n",
       "      <td>3154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open  close\n",
       "datetime               \n",
       "2016-11-21  2783   2714\n",
       "2016-11-22  2726   2900\n",
       "2016-11-23  2897   2954\n",
       "2016-11-24  2950   2951\n",
       "2016-11-25  2902   3154"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20150101', periods=5)\n",
    "fields = ['datetime','open','close']\n",
    "symbol = u'RB0000'\n",
    "# 获得日线数据\n",
    "tickers1 = loadHistoryData(DAILY_DB_NAME,symbol,'20161121','20161221',fields)\n",
    "tickers1.head()"
   ]
  }
 ],
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